Identifying Auditory Biomarkers for ALS

This project proposes a solution to identify auditory biomarkers to aid in the early diagnosis of ALS and improve voice recognition accuracy in automatic speech recognition software for such patients.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder that impacts the nervous system, affecting millions worldwide. However, diagnosing ALS early is difficult since symptoms are often confused for other disorders. ALS patients have difficulty completing simple tasks due to neuro-motor disruptions, making them dependent on automatic speech recognition (ASR) software for day-to-day tasks. Unfortunately, the strained, hypernasal voices of patients result in a 78% failure rate in such software. This project proposes a solution to identify auditory biomarkers for early diagnosis and a novel voice compensation method to improve voice recognition accuracy in ASR software. Auditory features were extracted and also used to generate images through a Mel-Frequency spectrogram. The auditory features and images were processed using an optimized Neural Network algorithm. The algorithm classifies voice files with an accuracy of 91% (auditory features) and 88% (spectrogram images). A new voice compensation algorithm was developed to adjust a word's duration, frequency, and pitch to improve the ASR recognition rate. This method successfully increases the recognition accuracy and allows ASR systems to understand voice commands from ALS patients easily. These methods can easily be applied to other neurological disorders to identify unique auditory biomarkers and create a voice compensation algorithm.

In the status quo, patients with different neurological disorders, such as ALS and Parkinson’s, are usually only diagnosed in the later stages of the disease primarily because the symptoms are often confused with common voice difficulties. Additionally, more than 90% of ALS diagnosed patients have no identifiable cause, generic history, or known diet issues. Doctors typically only diagnose patients when their symptoms persist and become more prevalent. However, one key symptom that starts earlier in patients with neurological disorders is dysarthria, a condition in which the muscles used for speech are weaker and patients have difficulty in controlling them. As a result, patients with neurological disorders are unable to say certain words or phrases or speak with a distinct vocal pattern that varies from people who do not suffer from these neurological disorders, causing issues with communication.  In the United States, someone suffers from a stroke every 40 seconds, and only about 10% of stroke victims recover completely, 25% recover with some minor impairments, but more than 40% have severe impairments needing special and long term care with limited ability to communicate to their loved ones. As the disease progresses, patients are sometimes left unable to do certain daily tasks and increasingly rely on technologies known as automatic speech recognition (ASR) software, such as Siri and Alexa. However, due to their changing vocal patterns, the software does not always recognize the needs and cannot assist the patient with completing daily tasks, making the quality of life for such patients harder.

This solution serves several different populations, mostly focusing on the elderly population and people who do not live in close proximity to hospitals. Most people develop ALS between the ages of 40-70 (als.org). However, the process for diagnosing ALS is incredibly time-consuming and very intensive since there are multiple tests to undergo, including spinal taps and electromyography (EMG). The cost of these tests can be very high, and patients may be required to take such invasive and time-consuming tests over a period of months and potentially years, which adds up to time, money, and energy that not all patients may not have. As a result, this solution seeks to make a common type of ALS testing, using one's voice, more accessible. This solution allows patients to save time, money, and energy as they no longer have to commute to the hospital to consult with doctors as frequently and no longer have to take as many expensive medical tests, making ALS testing more accessible and affordable to more people, especially the elderly and people who may not have access to a hospital or cannot afford extensive doctor's appointments. Additionally, patients with ALS have a hard time communicating with others due to changes in their vocal patterns. This hard time communicating with people leads many patients to often suffer from poor quality of life since they are unable to voice their needs. As a result, it is important to provide an accessible tool for patients to communicate and perform daily tasks.

Currently, I have read various scientific papers and articles discussing the diagnosis process. This background research helped me better understand the needs of the people undergoing the diagnosis process as well as patients themselves and their struggles to communicate with people. The next step I am currently working on is reaching out to different hospitals to get more information about what the ALS testing process is like in a hands-on, clinical setting and hearing from ALS patients on what their experiences have been like. I hope to use this experience to be a starting point of implementing this solution first in a clinical setting to verify its accuracy and feasibility before slowly transitioning to a means that is more accessible and affordable for patients. 

Currently, a sample working prototype has been developed. This prototype can currently work as-is since the data from ALS patients has been collected and the model has already been built. Ideally, the next step is to expand the prototype to other neurological disorders and consult medical professionals to ensure that this prototype would be feasible for patients to use. Once this step has been finished, the next step would be to create an app or a small device containing this model to start implementing it in real-world settings, such as in nursing homes, hospitals, and people's homes.

This project primarily relies on the use of different computer models and audio files to use audio data to make predictions on whether a patient has ALS or not. Using the Praat software, distinct audio features, such as Shimmer, Jitter, Pitch, Number of Pulses, Tempo, and Harmonicity, were extracted using a command-line script and observed for patients with and without ALS. Once this software filtered through the data, a Mel-Frequency Spectrogram process was used. The Mel-Frequency Spectrogram method involves using a Fourier Transformation on the raw voice file signals to convert it to several sets of images.

Another key component used in this solution is the Machine Learning component. Once the data has been cleaned through a series of filtering different features of a voice file (pitch, frequency, volume, etc.), the data is then fed through a machine learning algorithm, namely a stacked neural network model, that makes the final prediction of whether a patient has ALS or not. Additionally, the second aspect of this project relies on the creation of a "voice compensation" processing model that modifies an ALS patient's voice file by altering speed, pitch, and amplitude before sending this audio to an Automated Speech Recognition (ASR) software, such as Siri or Alexa. Once this modified audio file serves as an input for the ASR software, the software then better recognizes what a patient is asking for and can better suit their needs.

Since this solution is only a prototype and has not been launched yet, I hope to serve at least a few hundred patients by partnering with local hospitals and organizations committed to improving healthcare access. This number will hopefully be higher in the future, but first, I would like to take the necessary steps to consult with medical professionals to see if there could be any improvements to the technology before large-scale implementation. The number one priority is ensuring that this solution continues to provide accurate results in the real world and perhaps adding slight improvements to the technology to ensure that all patients benefit from this solution. First, after creating a pilot program to test out the feasibility of the solution in the real world, then I want to continue partnering with more local hospitals and perhaps international organizations to provide this solution to everyone around the world.

• Improve access to ALS testing for all people all over the world and quality of life for ALS patients
  • The primary objective of this project is to create systems to allow for the early diagnosis of ALS and to improve the quality of life for patients with ALS who primarily rely on ASR software to complete daily tasks. By creating tools to assist doctors in the early diagnosis of ALS, patients and doctors can take more measures to slow down the onset of ALS while maintaining independence by using ASRs to complete basic tasks

• Integrate this solution in communities around the world by creating an app or device with this technology
  • The primary way to integrate this solution in communities is by creating an app or device for people to connect to and use the test I created. With more and more people connected to the internet in recent years, more people can use tools that rely on software and the internet as long as they are provided with such tools.

• Partner with hospitals and organizations to provide more people with access to such technology
  • Hospitals and organizations that promote health equality have established connections with patients and other networks meant to support all patients with neurological disorders, including ALS. By connecting with hospitals and organizations, this solution can easily be integrated into existing networks where more people can be helped.

One primary metric I plan to use to measure my progress towards these goals is by looking at the number of users this solution has. Once partnering with local hospitals and organizations, this solution will have more users that have access to a simple app/device that they can then use to take these tests from the comfort of their home while being monitored by doctors and other healthcare professionals. Additionally, another metric I would like to use is the number of patients that have been diagnosed through this platform. The primary objective of this solution is to make ALS testing more accessible and affordable for everyone, and one such way to quantify this objective is by looking at how many diagnoses have taken place through this platform. Another metric I would also like to look at is the amount of time and money saved as a result of this solution. By gathering information about a user's location and the nearest hospital to them, the model can then use this information to estimate how much time and money was saved as a result of taking such tests at home rather than commuting to and from a hospital that may be inaccessible for people, especially the elderly. 

• Implementation of the solution
  • One of the primary barriers that currently exists is figuring out how I can implement this model in the real world through either an app or a device. I would first need to learn the technical skills needed to build this solution or find something with these skills before partnering with hospitals and organizations.
• Finding a team
  • Another main barrier is the current lack of a team working on this project. I want to start working with a team to offer different perspectives and have different skills to ensure that this final project is beneficial for everyone. I would want a team that could help me work on the technical side of things as well as do market research to understand what else is currently happening as well as find partner hospitals and organizations to work with.
• Liabilities
  • There potentially may be some liabilities when working with hospitals and organizations that I may need to take into consideration, ranging from HIPAA to the consent of patients. I want to ensure that there are no legal issues or liability issues when working with hospitals and organizations, so I want to have legal expertise to ensure that there are no issues.

Equality in healthcare is truly an issue that I care about deeply, and I have done research and volunteer work related to my passion. I have done several projects related to using AI and Machine Learning techniques in the healthcare field, such as creating a project to predict Parkinson's Disease as well as another project to predict emotions in non-verbal patients. These projects have taught me many technical skills, such as coding and a basic understanding of machine learning, as well as personal skills, such as resilience and focus.

Additionally, I have done volunteer work with the Tamil Nadu Foundation where I implemented a women’s health project in India for high school students, culminating in the education of 2000+ girls. I educated such girls regarding the importance of menstrual health and donated nearly 3000+ pads to schools in India. I am currently working with the TNF organization to discuss ideas to expand this project all throughout Southern India as well as how to create a sustainable initiative to continue the education of future generations.

I am also currently pursuing degrees in Bioengineering and Business with the hopes of creating more projects dedicated to promoting equal access to healthcare. I want to do everything I can to ensure that no one needs to suffer the consequences of not having accessible and affordable healthcare solutions. With my desire to improve the world and to learn new skills to bolster my desire for change, I want to do my part to make a difference in countless peoples’ lives.

Currently, I am not partnering with any organizations, but there are a few organizations and hospitals I would like to reach out to maximize the beneficial impact this solution can have on the world. One organization I would like to reach out to and work with is Partners in Health (https://www.pih.org/). This organization works to combat health inequality around the world by working with other organizations and governments to provide quality healthcare to local communities and also advocate for legislation to ensure equity in healthcare. Since my project is targeted to provide accessible and affordable healthcare to people around the world, I would want to reach out to Partners in Health who also believes and supports this mission of quality healthcare for all.

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In many underdeveloped nations, women and girls bear the primary burden of taking care of the family and fulfilling everyone's needs. As a result, many of them do not take the proper care they need to care for their health and well-being. This project seeks to make it easier for women to get quality access to healthcare that they need without having to undertake a massive burden of getting transportation and other resources they need. By creating such a system, women can take the steps they need to take care of their health.